现代显示
現代顯示
현대현시
ADVANCED DISPLAY
2012年
9期
325-330
,共6页
宋喜佳%刘维亚%陈伟%郑喜凤
宋喜佳%劉維亞%陳偉%鄭喜鳳
송희가%류유아%진위%정희봉
稀疏模型%正交匹配追踪%网状格点%迭代细化%均方根误差
稀疏模型%正交匹配追蹤%網狀格點%迭代細化%均方根誤差
희소모형%정교필배추종%망상격점%질대세화%균방근오차
sparse model%OMP%reticulated grids%iterative refinement%RMSE
在LED照明应用中,为了能够获得、记录或重现特定的照明模式,要求检测装置可以测量出每个LED灯点照明空间中某一位置时的独立”贡献量”.这可以归结为对驱动LED工作的脉宽调制波形(PWM)的参数(振幅、频率偏移量、相位延迟)估计问题。为了达到参数估计的目的。首先将频率偏移空间和相位延迟空间离散化成二维网状格点,然后根据检测装置测量得到的数据在格点空间具有稀疏性的特点建立稀疏模型。接着,采用正交匹配追踪算法(OMP),用很少的采样点快速有效地重建未知参数。最后.为了有效抑制估计误差。本文使用了一种逐级迭代细分网格的技术作为前面稀疏模型的补充。实验结果表明.本文方法仅使用相当于奈奎斯特采样定理要求的27.5%的少量采样点就完成了快速估计的任务,同时.不同噪声条件下的对比试验说明算法在信噪比大于20dB时鲁棒性较好。
在LED照明應用中,為瞭能夠穫得、記錄或重現特定的照明模式,要求檢測裝置可以測量齣每箇LED燈點照明空間中某一位置時的獨立”貢獻量”.這可以歸結為對驅動LED工作的脈寬調製波形(PWM)的參數(振幅、頻率偏移量、相位延遲)估計問題。為瞭達到參數估計的目的。首先將頻率偏移空間和相位延遲空間離散化成二維網狀格點,然後根據檢測裝置測量得到的數據在格點空間具有稀疏性的特點建立稀疏模型。接著,採用正交匹配追蹤算法(OMP),用很少的採樣點快速有效地重建未知參數。最後.為瞭有效抑製估計誤差。本文使用瞭一種逐級迭代細分網格的技術作為前麵稀疏模型的補充。實驗結果錶明.本文方法僅使用相噹于奈奎斯特採樣定理要求的27.5%的少量採樣點就完成瞭快速估計的任務,同時.不同譟聲條件下的對比試驗說明算法在信譟比大于20dB時魯棒性較好。
재LED조명응용중,위료능구획득、기록혹중현특정적조명모식,요구검측장치가이측량출매개LED등점조명공간중모일위치시적독립”공헌량”.저가이귀결위대구동LED공작적맥관조제파형(PWM)적삼수(진폭、빈솔편이량、상위연지)고계문제。위료체도삼수고계적목적。수선장빈솔편이공간화상위연지공간리산화성이유망상격점,연후근거검측장치측량득도적수거재격점공간구유희소성적특점건립희소모형。접착,채용정교필배추종산법(OMP),용흔소적채양점쾌속유효지중건미지삼수。최후.위료유효억제고계오차。본문사용료일충축급질대세분망격적기술작위전면희소모형적보충。실험결과표명.본문방법부사용상당우내규사특채양정리요구적27.5%적소량채양점취완성료쾌속고계적임무,동시.불동조성조건하적대비시험설명산법재신조비대우20dB시로봉성교호。
In order to acquire, record or reappear some special lighting pattern in LED illumination application, detection devices are required to measure the "contribution" of each LED point illuminating the scene of interest and target locations, which can come down to estimate the parameters (amplitude, frequency offset and phase delay) of pulse-width modulation (PWM) which drives LEDs illumining. To this end, the frequency offset-phase delay space is discrete into two-dimensional reticulated grids firstly, and then a sparsity model is built based on the character that the measured data from detection devices is sparsity. Next, employing orthogonal matching pursuit (OMP) algorithm, we only use very few samples to reconstruct the unknown parameters. Finally, to suppress estimation error efficiently, a technique of iterative refinement of grids is also presented as a complement of previous sparse model. Experimental results indicate that the method presented in this paper finishes the estimation task rapidly, using only 27.5% of samples which are needed in traditional Nyquist Sampling theorem. Simultaneously, we find that this algorithm is robustness when SNR is higher than 20dB through lots of contrast tests in different noise conditions.